My grandfather used to predict if it was going to rain based on the degree of discomfort he felt in his arthritic joints. If memory serves me, 90% of the time, he was right.
So, what does this have to do with the question of whether or not expertise and talent are scalable?
To answer this question, I refer to a recent post on LinkedIn by Richard Pennington in which he talks about the book Noise: A Flaw in Human Judgment.
It is difficult to sum up a well-written book in a few sentences. But we will do our best, starting with the following excerpt:
“You may believe that you are subtler, more insightful, and more nuanced than the linear caricature of your thinking. But in fact, you are mostly noisier.”
In referencing their model for creating “noiseless rules” by leveraging “simple mechanical calculations of historical data to develop predictions about future behavior,” the authors suggest that individual capabilities are equivalent to an aching knee forecasting the weather.
Their case reference regarding one person being sentenced to 15 years and another to 30 days despite committing the same crime is compelling. However, despite making a solid case for a standardized numerical approach to proposal evaluations, for example, not everyone is sold on using machine logic alone.
The “biggest problems with numerical evaluations,” according to Pennington, is “the absence of decision hygiene and a delegation to a spreadsheet or other electronic tool to make the ultimate value decision.”
Pennington is saying that technology is supposed to free up procurement professionals from mundane and repetitive tasks to focus on more strategic activities.
We will go into greater detail regarding the subject of decision hygiene and human behavior in the decision-making process in a future post. However, what is very disconcerting is the continuing insistence on either or when it comes to humans and machines.
For example, just because technology can free us from the previously mentioned mundane tasks of our daily duties doesn’t mean that we should turn our backs on such activities.
Conversely, even though technology will remove the subjective nature of proposal assessment, providing a “calculated” outcome doesn’t mean humans have nothing more to add to the equation.
While acknowledging that this is a nuanced issue, Pennington suggests that this lack of balance between human and machine is the main reason that the U.S. Department of Defense has “moved away from numerical evaluations.”
What or where is the balance between a fallible aching human knee and the infallible computations of a machine?
We believe that the pendulum does not have to swing from one extreme to the next. In this context of non-extremes, we are really talking about scalable standards that create certain—or more certain and equitable—outcomes regarding proposals. In other words, people and machines working together by leveraging each other’s strengths.
Now that is logical.